1. Generating surrogate depth data (Fig REF a).  Our training of a depth decoder requires the underlying ground-truth depth maps of natural images.
To obtain these data we generate depth maps by feeding-forward the natural images through
an off-the-shelf monocular depth estimation method for natural images, called MiDaS [1]}.
We then use these maps as our surrogate “ground-truth” depth data. We estimated the depth maps of all 1250 images of `fMRI on ImageNet' [2]}, and all those of ImageNet classification challenge (ILSVRC) [3]}.
These depth-maps are then used in the following steps.
